传感器网络中数据时新性移动设备的调度

为了在传感器网络中收集时间敏感性的数据,引入了移动设备来收集数据。提出了两种启发式算法,一种是基于货郎担问题的解法,将原问题分割成较小集合,然后逐步求解小问题,该算法适用于数据敏感性要求相对较低的应用;而当数据敏感性要求较高时,提出的贪婪式算法逐步建立移动设备的移动路径,即从基站(Sink)开始迭代选择代价值最小的节点,直到不能再添加节点进移动路径中。理论分析和模拟结果表明,提出的算法可以减少数据收集过程中所需要的移动设备的数目,而且大大节省了数据收集的总时间,从而可以应用在大规模网络中。     

Querying distributed multimedia databases and data sources for sensor data fusion

Sensor data fusion imposes a number of novel requirements on query languages and query processing techniques. A spatial/temporal query language called /spl Sigma/QL has been proposed to support the retrieval and fusion of multimedia information from multiple sources and databases. In this paper we investigate fusion techniques, multimedia data transformations and /spl Sigma/QL query processing techniques for sensor data fusion. Fusion techniques including fusion by the merge operation, the detection of moving objects, and the incorporation of belief values, have been developed. An experimental prototype has been implemented and tested to demonstrate the feasibility of these techniques.     

Managing sensor traffic data and forecasting unusual behaviour propagation

Sensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static (fixed) and mobile (embedded) sensors, generating large and complex spatio-temporal series. This scenario presents several research challenges, in spatio-temporal data management and data analysis. Management issues involve, for instance, data cleaning and data fusion to support queries at distinct spatial and temporal granularities. Analysis issues include the characterization of traffic behavior for given space and/or time windows, and detection of anomalous behavior (either due to sensor malfunction, or to traffic events). This paper contributes to the solution of some of these issues through a new kind of framework to manage static sensor data. Our work is based on combining research on analytical methods to process sensor data, and data management strategies to query these data. The first aspect is geared towards supporting pattern matching. This leads to a model to study and predict unusual traffic behavior along an urban road network. The second aspect deals with spatio-temporal database issues, taking into account information produced by the model. This allows distinct granularities and modalities of analysis of sensor data in space and time. This work was conducted within a project that uses real data, with tests conducted on 1,000 sensors, during 3 years, in a large French city.     

A data collection protocol for real-time sensor applications

The nature of many sensor applications as well as continuously changing sensor data often imposes real-time requirements on wireless sensor network protocols. Due to numerous design constraints, such as limited bandwidth, memory and energy of sensor platforms, and packet collisions that can potentially lead to an unbounded number of retransmissions, timeliness techniques designed for real-time systems and real-time databases cannot be applied directly to wireless sensor networks. Our objective is to design a protocol for sensor applications that require periodic collection of raw data reports from the entire network in a timely manner. We formulate the problem as a graph coloring problem. We then present TIGRA (Timely Sensor Data Collection using Distributed Graph Coloring) — a distributed heuristic for graph coloring that takes into account application semantics and special characteristics of sensor networks. TIGRA ensures that no interference occurs and spatial channel reuse is maximized by assigning a specific time slot for each node. Although the end-to-end delay incurred by sensor data collection largely depends on a specific topology, platform, and application, TIGRA provides a transmission schedule that guarantees a deterministic delay on sensor data collection.     

End-to-end deadline control for aperiodic tasks in distributed real-time systems

A category of Distributed Real-Time Systems (DRTS) that has multiprocessor pipeline architecture is increasingly used. The key challenge of such systems is to guarantee the end-to-end deadlines of aperiodic tasks. This paper proposes an end-to-end deadline control model, called Linear Quadratic Stochastic Optimal Control Model (LQ-SOCM), which features a distributed feedback control that dynamically enforces the desired performance. The control system considers the aperiodic task arrivals and execution times’ variation as the two external factors of the system unpredictability. LQ-SOCM uses discrete time state space equation to describe the real-time computing system. Then, in the actuator design, a continuous manner is adopted to deal with discrete QoS (Quality of Service) adaptation. Finally, experiments demonstrate that the system is globally stable and can statistically provide the end-to-end deadline guarantee for aperiodic tasks. At the same time, LQ-SOCM is capable of effectively improving the system throughput.

Priority inheritance in soft real-time databases

Due to resource sharing among tasks, priority inversion can occur during priority-driven preemptive scheduling. In this work, we investigate solutions to the priority inversion problem in a soft real-time database environment where two-phhse locking is employed for concurrency control. We examine two basic schemes for addressing the priority inversion problem, one based on priority inheritance and the other based on priority abort. We also study a new scheme, called conditional priority inheritance, which attempts to capitalize on the advantages of each of the two basic schemes. In contrast with previous results obtained in real-time operating systems, our performance studies, conducted on an actual real-time database testbed, indicate that the basic priority inheritance protocol is inappropriate for solving the priority inversion problem in real-time database systems. We identify the reasons for this performance. We also show that the conditional priority inheritance scheme and the priority abort scheme perform well for a wide range of system workloads.This work was supported by the National Science Foundation under Grant IRI-8908693 and by the U.S. Office of Naval Research under Grant N00014-85-K0398.A previous version of this paper appeared in Real-Time Systems Symposium, Dec. 1991.     

Dynamic sensor data segmentation for real-time knowledge-driven activity recognition

Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The paper first analyzes the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83% in all experiments for real time activity recognition, which proves the approach and the underlying model.     

Priority assignment in real-time active databases

Active databases and real-time databases have been important areas of research in the recent past. It has been recognized that many benefits can be gained by integrating real-time and active database technologies. However, not much work has been done in the area of transaction processing in real-time active databases. This paper deals with an important aspect of transaction processing in real-time active databases, namely the problem of assigning priorities to transactions. In these systems, time-constrained transactions trigger other transactions during their execution. We present three policies for assigning priorities to parent, immediate and deferred transactions executing on a multiprocessor system and then evaluate the policies through simulation. The policies use different amounts of semantic information about transactions to assign the priorities. The simulator has been validated against the results of earlier published studies. We conducted experiments in three settings: a task setting, a main memory database setting and a disk-resident database setting. Our results demonstrate that dynamically changing the priorities of transactions, depending on their behavior (triggering rules), yields a substantial improvement in the number of triggering transactions that meet their deadline in all three settings. Edited by Henry F. Korth and Amith Sheth. Received November 1994 / Accepted March 20, 1995     

Update propagation strategies to improve freshness in lazy master replicated databases

Many distributed database applications need to replicate data to improve data availability and query response time. The two-phase commit protocol guarantees mutual consistency of replicated data but does not provide good performance. Lazy replication has been used as an alternative solution in several types of applications such as on-line financial transactions and telecommunication systems. In this case, mutual consistency is relaxed and the concept of freshness is used to measure the deviation between replica copies. In this paper, we propose two update propagation strategies that improve freshness. Both of them use immediate propagation: updates to a primary copy are propagated towards a slave node as soon as they are detected at the master node without waiting for the commitment of the update transaction. Our performance study shows that our strategies can improve data freshness by up to five times compared with the deferred approach. Received April 24, 1998 / Revised June 7, 1999     

嵌入式Web网络传感器的实时数据采集方法

实时获取传感器数据是基于Internet测控系统的关键技术之一,对Web传感器的远程实时数据采集方法,国内报道的不多.基于CGI和Java Applet技术实现了远程传感器数据的实时采集,通过数字签名技术实现了客户端的数据存储,并应用到了实际的传感器,具有一定的实用性.     

Temporal and real-time databases: a survey

A temporal database contains time-varying data. In a real-time database transactions have deadlines or timing constraints. In this paper we review the substantial research in these two previously separate areas. First we characterize the time domain; then we investigate temporal and real-time data models. We evaluate temporal and real-time query languages along several dimensions. We examine temporal and real-time DBMS implementation. Finally, we summarize major research accomplishments to date and list several unanswered research questions     

结合价值密度和截止期的网格实时作业调度

在商业网格和云计算环境中,作业有到达时间、计算量、预算、截止期等参数,其中,预算是时间的函数。准确区分作业的重要性和紧迫性是作业调度系统的一个关键问题。综合利用这四个参数来定义作业的优先级,并提出基于价值密度和相对截止期的网格作业调度算法。分别对弱实时和强实时网格作业的调度进行仿真。仿真结果显示,所提出的调度算法的性能在两种情况下都优于所有对比算法的性能,且在强实时作业情况下优势更明显。     

Worst-case deadline failure probability in real-time applications distributed over controller area network

Real-time applications distributed over the controller area network (CAN) are generally characterised by stringent temporal and dependability constraints. Our goal is to take account of transmission errors in the design of such applications because the consequences of such disturbances are potentially disastrous. In this study, the concept of worst-case deadline failure probability (WCDFP) is introduced. The motivation of the probabilistic approach is that, in practice, the number of errors occurring during a given time period can with difficulty be bounded. To evaluate the WCDFP, we propose, on the one hand, a method of computing for each message the tolerable threshold of transmission errors under which timing constraints are guaranteed to be met. On the other hand, we also suggest an error model enabling us to consider both error frequency and error gravity. Our error model follows a generalized Poisson process and its stochastic parameters have been derived. We then propose a numerically efficient algorithm to compute the probabilities and apply the analysis to an industrial case-study of the automotive field.     

Convex optimization framework for intermediate deadline assignment in soft and hard real-time distributed systems

It is generally challenging to determine end-to-end delays of applications for maximizing the aggregate system utility subject to timing constraints. Many practical approaches suggest the use of intermediate deadline of tasks in order to control and upper-bound their end-to-end delays. This paper proposes a unified framework for different time-sensitive, global optimization problems, and solves them in a distributed manner using Lagrangian duality. The framework uses global viewpoints to assign intermediate deadlines, taking resource contention among tasks into consideration. For soft real-time tasks, the proposed framework effectively addresses the deadline assignment problem while maximizing the aggregate quality of service. For hard real-time tasks, we show that existing heuristic solutions to the deadline assignment problem can be incorporated into the proposed framework, enriching their mathematical interpretation.     

Partitioned data objects in distributed databases

In many distributed databases locality of reference is crucial to achieve acceptable performance. However, the purpose of data distribution is to spread the data among several remote sites. One way to solve this contradiction is to use partitioned data techniques. Instead of accessing the entire data, a site works on a fraction that is made locally available, thereby increasing the site's autonomy. We present a theory of partitioned data that formalizes the concept and establishes the basis to develop a correctness criterion and a concurrency control protocol for partitioned databases. Set-serializability is proposed as a correctness criterion and we suggest an implementation that integrates partitioned and non-partitioned data. To complete this study, the policies required in a real implementation are also analyzed. Recommended by: Hector Garcia-Molina     

Set-oriented data mining in relational databases

Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data is needed. In such an approach, the data mining operations are expressed in terms of relational or set-oriented operations. Query optimization technology can then be used for efficient processing.

In this paper, we describe set-oriented algorithms for mining association rules. Such algorithms imply performing multiple joins and thus may appear to be inherently less efficient than special-purpose algorithms. We develop new algorithms that can be expressed as SQL queries, and discuss optimization of these algorithms. After analytical evaluation, an algorithm named SETM emerges as the algorithm of choice. Algorithm SETM uses only simple database primitives, viz., sorting and merge-scan join. Algorithm SETM is simple, fast, and stable over the range of parameter values. It is easily parallelized and we suggest several additional optimizations. The set-oriented nature of Algorithm SETM makes it possible to develop extensions easily and its performance makes it feasible to build interactive data mining tools for large databases.     

Design and implementation of a cloud-based event-driven architecture for real-time data processing in wireless sensor networks

The growth of the Internet of Things (IoTs) and the number of connected devices is driven by emerging applications and business models. One common aim is to provide systems able to synchronize these devices, handle the big amount of daily generated data and meet business demands. This paper proposes a cost-effective cloud-based architecture using an event-driven backbone to process many applications’ data in real-time, called REDA. It supports the Amazon Web Service (AWS) IoT core, and it opens the door as a free software-based implementation. Measured data from several wireless sensor nodes are transmitted to the cloud running application through the lightweight publisher/subscriber messaging transport protocol, MQTT. The real-time stream processing platform, Apache Kafka, is used as a message broker to receive data from the producer and forward it to the correspondent consumer. Micro-services design patterns, as an event consumer, are implemented with Java spring and managed with Apache Maven to avoid the monolithic applications’ problem. The Apache Kafka cluster co-located with Zookeeper is deployed over three availability zones and optimized for high throughput and low latency. To guarantee no message loss and to simulate the system performances, different load tests are carried out. The proposed architecture is reliable in stress cases and can handle records goes to 8000 messages in a second with low latency in a cheap hosted and configured architecture.

     

Power- and time-aware buffer cache management for real-time embedded databases

Due to the explosive increases of data from both the cyber and physical worlds, the demand for database support in embedded systems is increasing. Databases for embedded systems, or embedded databases, are expected to provide timely in situ data services under various resource constraints, such as limited energy. However, traditional buffer cache management schemes, in which the primary goal is to minimize the number of I/O operations, is problematic since they do not consider the constraints of modern embedded devices such as limited energy and distinctive underlying storage. In particular, due to asymmetric read/write characteristics of flash memory-based storage of modern embedded devices, minimum buffer cache misses neither coincide with minimum power consumption nor minimum I/O deadline misses. In this paper we propose a novel power- and time-aware buffer cache management scheme for embedded databases. A novel multi-dimensional feedback control architecture is proposed and the characteristics of underlying storage of modern embedded devices is exploited for the simultaneous support of the desired I/O power consumption and the I/O deadline miss ratio. We have shown through an extensive simulation that our approach satisfies both power and timing requirements in I/O operations under a variety of workloads while consuming significantly smaller buffer space than baseline approaches.