Guohui Li

Energy-Efficient Scheduling in Nonpreemptive Systems With Real-Time Constraints

In the past decade, the development of mobile and embedded systems has demanded energy efficiency for improving the lifetime of embedded devices. To avoid preemption overhead or ease timing verification, nonpreemptive scheduling has been deemed useful or necessary in meeting system timing requirements for certain applications built on embedded devices. In this paper, our aim is to design nonpreemptive scheduling algorithms that ensure timing correctness and optimize energy consumption on a processor with variable speeds. We propose a representative algorithm, ISA, which can produce lower speeds for a variety of nonpreemptive task sets than other comparable methods, and hence resulting in significant energy savings. When combined with a selective frequency-inheritance policy we design to efficiently determine if processor speedup can be disabled without jeopardizing any task deadlines, ISA can achieve even larger gains, up to 30% reduction in energy consumption. Finally, we propose a dynamic slack reclamation policy built on ISA, namely ISA-DR, which can result in additional energy savings when a task consumes less than its worst-case execution time.

A Novel Iterative Multilateral Localization Algorithm for Wireless Sensor Networks

In many applications of wireless sensor networks, location is very important information. It can be used to identify the location at which sensor readings originate, in routing and data storage protocols based on geographical areas and so on. Location information can come from manual setting or GPS device. However, manual setting requires huge cost of human time, and GPS setting requires expensive device cost. Both approaches are not applicable to localization task of large scale wireless sensor networks. In this paper, we propose an accurate and efficient localization algorithm, called iterative multilateral localization algorithm based on time rounds. This Algorithm uses time round mechanism and anchor nodes triangle placement scheme to reduce error accumulation caused by iteratively localization. And it also reduces location errors and prevents abnormal phenomena caused by trilateral localization through limiting the minimum number of neighboring beacon nodes used in different localizing time rounds. Experimental results reveal that this algorithm has high localization accuracy, even if in large range errors, it can achieve good result.

Vague continuous K-nearest neighbor queries over moving objects with uncertain velocity in road networks

Recent research has focused on Continuous K Nearest Neighbor (CKNN) queries in road networks, where the queries and the data objects are moving. Most existing approaches assume the fixed velocity of moving objects. The release of fixed moving velocity makes the query process slowly due to the significant repetitive query cost. In this paper, we study CKNN queries over moving objects with uncertain velocity in road networks. A Distance Interval Model (DIM) is designed to calculate the minimal and maximal road network distances between moving objects and query point. Furthermore, we propose a novel Possibility-based Vague KNN (PVKNN) algorithm to process the query efficiently, which determines the CKNN query results with possibility within each division time subinterval of given time interval by applying the vague set theory. In the PVKNN algorithm, the query efficiency can be improved significantly with the pruning, distilling and possibility-ranking phases. With these phases, the objects candidates are scaled down and the given time interval is divided into subintervals to reduce the repetitive query cost. In addition, an index structure TPR^u^v-Tree is designed to efficiently index moving objects with uncertain velocity in road network by involving edge connection and moving objects information. Experiments with simulation and comparison show that significant improvement in the performance of efficiency can be achieved with our proposed algorithms.

Workload-Aware Partitioning for Maintaining Temporal Consistency upon Multiprocessor Platforms

Deriving deadlines and periods of update transactions for maintaining timeliness and data freshness has long been recognized as an important problem in real-time database research. Despite years of active research, the state of the art only focuses on uniprocessor systems. In this paper, we take a first step of studying the workload-aware temporal consistency maintenance problem upon multiprocessor platforms. We consider the problem of how to partition a set of update transactions to

A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information

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Efficient decomposition of strongly connected components on GPUs

processors to maintain the temporal consistency of real-time data objects under earliest deadline first (EDF) scheduling, while minimizing the total workload on

Searching continuous nearest neighbors in road networks on the air

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Workload-Efficient Deadline and Period Assignment for Maintaining Temporal Consistency under EDF

processors. Firstly, we only consider the feasibility aspect of the problem by proposing a polynomial time partitioning scheme, Temporal Consistency Partitioning (TCP), and formally showing that the resource augmentation bound of TCP is

A Novel Scheduling Algorithm for Supporting Periodic Queries in Broadcast Environments

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Network Voronoi Diagram on uncertain objects for nearest neighbor queries

. Secondly, we address the partition problem globally by proposing a polynomial time heuristic, Density factor Balancing Fit (DBF), where density factor balancing plays a major role in producing workload-efficient partitionings. Finally, we evaluate the feasibility and workload performances of DBF versus other heuristics with comparable quality experimentally.