Dependent data broadcasting for unordered queries in a multiple channel mobile environment

Data broadcast is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. In many applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data are restricted to a single broadcast channel environment, and as a consequence, the results are of limited applicability to the upcoming mobile environments. In view of this, we relax this restriction and explore the problem of broadcasting dependent data in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs. Our experimental results show that the theoretical results derived are able to guide the search of the genetic algorithm very effectively, thus leading to broadcast programs of very high quality.     

An Efficient Algorithm for Near Optimal Data Allocation on Multiple Broadcast Channels

In a wireless environment, the bandwidth of the channels and the energy of the portable devices are limited. Data broadcast has become an excellent method for efficient data dissemination. In this paper, the problem for generating a broadcast program of a set of data items with the associated access frequencies on multiple channels is explored. In our approach, a minimal expected average access time of the broadcast data items is first derived. The broadcast program is then generated, which minimizes the minimal expected average access time. Simulation is performed to compare the performance of our approach with two existing approaches. The result of the experiments shows that our approach outperforms others and is in fact close to the optimal.     

On the Optimal Robot Routing Problem in Wireless Sensor Networks

In recent years, data broadcasting has become a promising technique to design a mobile information system with power conservation, high scalability, and high bandwidth utilization. In many applications, the query issued by a mobile client corresponds to multiple items that should be accessed in a sequential order. In this paper, we study the scheduling approach in such a sequential data broadcasting environment. Explicitly, we propose a general framework referred to as MULS (standing for MUltiLevel Service) for an information system. There are two primary stages in MULS: online scheduling (OLS) and optimization procedure. In the first stage, we propose an OLS algorithm to allocate the data items into multiple channels. As for the second stage, we devise an optimization procedure, called sampling with controlled iteration (SCI), to enhance the quality of broadcast programs generated by algorithm OLS. Procedure SCI is able to strike a compromise between effectiveness and efficiency by tuning the control parameters. According to the experimental results, we show that algorithm OLS with procedure SCI outperforms the approaches in prior works prominently in both effectiveness (that is, the average access time of mobile users) and efficiency (that is, the complexity of the scheduling algorithm). Therefore, by cooperating algorithm OLS with procedure SCI, the proposed MULS framework is able to generate broadcast programs with the flexibility of providing different service qualities under different requirements of effectiveness and efficiency: in the dynamic environment in which the access patterns and information contents change rapidly, the parameters used in SCI will perform OLS with satisfactory service quality. As for the static environment in which the query profile and the database are updated infrequently, larger values of parameters are helpful to generate an optimized broadcast program, indicating the advantageous feature of MULS.     

MULS: A General Framework of Providing Multilevel Service Quality in Sequential Data Broadcasting

In recent years, data broadcasting becomes a promising technique to design a mobile information system with power conservation, high scalability and high bandwidth utilization. In many applications, the query issued by a mobile client corresponds to multiple items which should be accessed in a sequential order. In this paper, we study the scheduling approach in such a sequential data broadcasting environment. Explicitly, we propose a general framework referred to as MULS (standing for MUlti-Level Service) for an information system. There are two primary stages in MULS: on-line scheduling and optimization procedure. In the first stage, we propose an On- Line Scheduling algorithm (denoted by OLS) to allocate the data items into multiple channels. As for the second stage, we devise an optimization procedure SCI, standing for Sampling with Controlled Iteration, to enhance the quality of broadcast programs generated by algorithm OLS. Procedure SCI is able to strike a compromise between effectiveness and efficiency by tuning the control parameters. According to the experimental results, we show that algorithm OLS with procedure SCI outperforms the approaches in prior works prominently in both effectiveness (i.e., the average access time of mobile users) and efficiency (i.e., the complexity of the scheduling algorithm). Therefore, by cooperating algorithm OLS with procedure SCI, the proposed MULS framework is able to generate broadcast programs with flexibility of providing different service qualities under different requirements of effectiveness and efficiency: in the dynamic environment in which the access patterns and information contents change rapidly, the parameters used in SCI will perform online scheduling with satisfactory service quality. As for the static environment in which the query profile and the database are updated infrequently, larger values of parameters are helpful to generate an optimized broadcast program, indicating the advantageous feature of MULS.     

移动实时环境下一种改进的广播调度算法

在网络带宽不对称的移动实时环境中,数据广播是一种有效的数据访问方式。针对这种网络特性,分析了现今已经存在的某些广播调度算法。针对UFO算法,分别提出了SBS算法和CRS算法,它们从服务器、移动客户端两个方面进行了改进。两种算法可以根据给定的数据项访问概率分布,自动生成广播调度。通过理论分析和实验结果表明,该算法不会产生事务重启,并且可以有效减少数据的访问时间,使用户访问数据广播的平均等待时间最小。     

Generating broadcast programs that support range queries

To disseminate information via broadcasting, a data server must construct a broadcast “program” that meets the needs of the client population. Existing works on generating broadcast programs have shown the effectiveness of nonuniform broadcast programs in reducing the average access times of objects for nonuniform access patterns. However, these broadcast programs perform poorly for range queries. The article presents a novel algorithm to generate broadcast programs that facilitate range queries without sacrificing much on the performance of single object retrievals     

Object Organization on a Single Broadcast Channel in the Mobile Computing Environment

Advances in computation and communication technologies allow users to access computer networks, using portable computing devices via a wireless connection, while mobile. Furthermore, multidatabases offer practical means of managing information sharing from multiple preexisting heterogeneous databases. By superimposing the mobile computing environment onto the multidatabase system, a new computing environment is attained.In this work, we concentrate on the effects of the mobile-computing environment on query processing in multidatabases. We show how broadcasting, as a possible solution, would respond to current challenges such as bandwidth and storage limitations. Organizing data objects along a single-dimension broadcast channel should follow the semantic links assumed within the multiple-dimension objects' structure. Learning from our past experiences in objects organization on conventional storage mediums (disks), we propose schemes for organizing objects along a single broadcast air channel. The proposed schemes are simulated and analyzed.     

Temperature prediction using fuzzy time series

A drawback of traditional forecasting methods is that they can not deal with forecasting problems in which the historical data are represented by linguistic values. Using fuzzy time series to deal with forecasting problems can overcome this drawback. In this paper, we propose a new fuzzy time series model called the two-factors time-variant fuzzy time series model to deal with forecasting problems. Based on the proposed model, we develop two algorithms for temperature prediction. Both algorithms have the advantage of obtaining good forecasting results.     

Data broadcasting for dependent information using multiple channels in wireless broadcast environments

Data broadcasting is an effective approach to disseminating information to mobile clients and has attracted much research attention in recent years. In many applications, the access pattern among the data can be represented by a weighted DAG. In this paper, we consider the problem of efficiently generating the broadcast schedules on multiple channels when the data set has a DAG access pattern. We show that it is NP-hard to find an optimal broadcast schedule which not only minimizes the latency but also satisfies the ancestor property that retains the data dependency. We further derive a condition for the input DAGs under which one can generate an optimal broadcast schedule in linear time and propose an algorithm to generate the schedule. Due to the NP-completeness, we provide three heuristics for general DAGs based on the level of a vertex in the input DAGs and each heuristic uses a different policy to place vertices into the broadcast channels. There are two categories for the policies. The first category mainly considers the probability for a process to stop at a considered vertex. The second category takes the vertices which are affected most when assigning a vertex into consideration. We analyze and discuss these heuristics. A short experimental simulation is given for supporting and validating the discussion. In particular, the experimental results indicate that roughly considering the whole posterior vertices of each vertex is not precise and may not lead to good results and considering the vertices affected most when assigning a vertex will help reducing the latency.     

Optimal Content Distribution in Video-on-Demand Tree Networks

Video-on-demand (VOD) services are expected to grow significantly over time, providing diverse programs for home entertainment, distance instruction, news on demand, and other applications. These services require large bandwidth resources. We present a model for a tree network where each node may have demands for multiple different VOD program families, where each program family is an aggregation of similar programs. The decision variables include location of VOD servers and assignment of program families to servers. The model considers cost of servers, cost of assigning program families to servers, and cost of link bandwidths used to broadcast programs. The objective is to minimize the sum of all these costs. The model is formulated as an integer program. We develop a dynamic programming formulation with multiple state variables and an algorithm that solves this model. Starting from the end nodes of the tree network, the algorithm determines optimal solutions to subtrees, eventually reaching the root node with the central server, thus providing an optimal solution to the entire network.     

实时环境下一种混合广播调度策略

在实时的非对称通讯环境下,自适应混合广播策略能够根据数据的请求模式以及时间限制等特征来动态分配周期广播和按需广播的带宽比例.将这一策略推广到基于事务的多数据项广播调度中,同时引入"分布式广播"思想,以更精细的周期广播粒度来解决过长的周期广播与事务及数据的实时要求之间的矛盾,并且动态分配时间槽.实验结果表明:改进的调度策略更适合于实时环境下的数据广播,具有更低的事务失败率以及更小的上行信道负荷.     

Linear Rate Control and Optimum Statistical Multiplexing for H.264 Video Broadcast

The H.264 video coding standard achieves significantly improved video compression efficiency and finds important applications in digital video broadcast. To enable H.264 video encoding for digital TV broadcast and maximize its broadcast efficiency, there are two important issues that need to be adequately addressed. First, we need to understand the complex coding mechanism of an H.264 video encoder and develop a model to analyze and control its rate-distortion (R-D) behavior in an accurate and robust manner. Second, the R-D behaviors of individual channels in the broadcast system should be jointly controlled and optimized under bandwidth and buffer constraints so as to maximize the overall broadcast quality. In this paper, we develop a linear rate model and a linear rate control scheme for H.264 video coding. We develop an optimum statistical multiplexing system to allocate bits across video programs (each being encoded by an H.264 encoder) and video frames so that the overall video broadcast quality is maximized. We study the bandwidth and buffer constraints in video broadcast and formulate the optimum statistical multiplexing into a constrained mathematical optimization problem. Realizing that it is impossible to find a close-form solution for global optima, we propose a simple yet efficient algorithm to find a near-optimum solution for joint rate allocation under buffer constraints. Our extensive simulation results demonstrate that the proposed statistical multiplexing system achieves about 40–50% saving in bandwidth, provides a smooth video quality change across programs and frames, and maintains robust decoder buffer control.      

Smooth workload adaptive broadcast

The high-bandwidth requirements and long-lived characteristics of digital video make transmission bandwidth usage a key limiting factor in the widespread streaming of such content over the Internet. A challenging problem is to develop bandwidth-efficient techniques for delivering popular videos to a large, asynchronous client population with time-varying demand characteristics. In this paper, we propose smooth workload adaptive broadcast to address the above issues. A key component of our scheme is Flexible Periodic Broadcast (FPB). By introducing a feedback control loop into FPB, and enhancing FPB using techniques such as parsimonious transmission, smooth workload adaptive broadcast provides instantaneous or near-instantaneous playback services and can smoothly adapt to workload changes. Furthermore, FPB, as proposed in this paper, is bandwidth efficient and exhibits the periodic smooth channel transition property.     

基于HLA的分布式仿真框架自动生成技术

针对基于HLA仿真应用系统开发中存在的计算模型程序与RTI服务程序的耦合问题,研究了基于共享内存和中断回调技术的解耦方法,进一步采用HLA仿真适配器对RTI服务程序进行封装,从而实现了计算模型程序与RTI底层服务程序的有效隔离。为提高仿真应用程序开发效率,分析了联邦成员中RTI服务程序功能需求,研究实现了仿真适配器自动生成软件。该软件可快速生成基于HLA的联邦成员适配器代码和执行程序,从而构建仿真应用系统分布式仿真框架。     

Design of an interactive video-on-demand system

We design an interactive video-on-demand (VOD) system using both the client-server paradigm and broadcast delivery paradigm. Between the VOD warehouse and customers, we adopt a client-server paradigm to provide an interactive service. Within the VOD warehouse, we adopt a broadcast delivery paradigm to support many concurrent customers. In particular, we exploit the enormous bandwidth of optical fibers for broadcast delivery, so that the system can provide many video programs and maintain a small access delay. In addition, we design and adopt an interleaved broadcast delivery scheme, so that every video stream only requires a small buffer size for temporary storage. A simple proxy is allocated to each ongoing customer, and it retrieves video from optical channels and delivers video to the customer through an information network. The proposed VOD system is suitable for large scale applications with many customers, and has several desirable features: 1) it can be scaled up to serve more concurrent customers and provide more video programs, 2) it provides interactive operations, 3) it only requires point-to-point communication between the VOD warehouse and the customer and involves no network control, 4) it has a small access delay, and 5) it requires a small buffer size for each video stream.     

On Bandwidth-Efficient Data Broadcast

In this paper, we leverage network coding to reduce the bandwidth consumption for data broadcast. We use the concept of mixing in a different way from the traditional network coding. Traditional network coding mixes all data items together. However, we mix each data item from only some data items according to the queried and stored data of receivers. Therefore, each receiver in our approach is required to receive fewer coded data to decode the required information, and the sender thereby is able to broadcast fewer data items. We formulate an optimization problem with integer-linear programming to minimize the bandwidth consumption in data broadcast. We prove that the problem is NP-hard and design an approximation algorithm that can be implemented in the data server. In addition, we show that different mixings of data items lead to different decoding costs for receivers. We design an algorithm to optimally code the data items with the minimum decoding cost.     

A speculative load adaptive streaming scheme for efficient video delivery

Video on demand services require video broadcast schemes to provide efficient and reliable performance under various client request loads. In this paper, we have developed an efficient request load adaptive broadcast scheme, speculative load adaptive streaming scheme (SLAS), that requires lower service bandwidth than previous approaches, regardless of request rate. We have provided both analysis and simulation to show the performance gain over previous schemes. In this paper, we provide the theoretic upper bound of the continuous segment allocations on channels. We found that the number of allocated segments of the SLAS is close to the theoretic upper bound when compared with other schemes over various numbers of stream channels. Our analysis of client waiting time is almost identical to simulation results about all client requests. By simulation, we compared the required service bandwidth and storage requirements of the SLAS scheme and other schemes and found the SLAS scheme is an efficient broadcast scheme as compared to well known seamless channel transition schemes.     

多盘广播的偏斜索引算法

在移动环境中,多盘广播是被广泛使用的数据广播调度算法之一。为广播数据建立索引可以使移动客户机选择性地监听信道,以减少电源消耗,但过多地插入索引会增加数据访问时间。针对该问题,为多盘广播建立偏斜索引,设计索引树构造算法和广播数据的索引树算法。与可变扇出算法相比,多盘广播的偏斜索引算法需要的平均访问时间和平均调谐时间较短。     

An energy efficient re-access scheme for data caching in data broadcast of a mobile computing environment

Data caching is used to improve the response time and the power consumption of a mobile client in a mobile computing environment. To enhance the performance of data caching, one needs to improve the hit ratio and to reduce the cost in processing a cache miss. In a mobile computing environment, a cached data item of a mobile client needs to remain up-to-date with respect to its corresponding data item in the server. A cached data item which is out of date is called a cached invalidated data item. Accessing a cached invalidated data item can be regarded as processing a cache miss. To access a cached invalidated data item, a mobile client needs to download the new content of the data item from the broadcast channel. This operation is called a re-access operation in this paper. Re-accessing a cached invalidated data item incurs large tuning time overhead. In this paper, we propose a re-access scheme that reduces this overhead by allowing a mobile client to access a cached invalidated data item from the broadcast channel without accessing indices. We analyze the performance of the proposed scheme and validate the analysis through experiments. The experiments showed that the proposed scheme significantly reduces the tuning time of a mobile client. Furthermore, the proposed scheme is robust in the sense that it allows changes on the broadcast structure in data broadcasting.