Optimal Universal Schedules for Discrete Broadcast

We study broadcast systems that distribute a series of data updates to a large number of passive clients. The updates are sent over a broadcast channel in the form of discrete packets. We assume that clients periodically access the channel to obtain the most recent update. Such scenarios arise in many practical applications, such as distribution of traffic information and market updates to mobile wireless devices

Data broadcast scheduling: Models, algorithms, and analysis

Inherent in the field of data broadcasting is a communication problem in which a server is to transmit a subset of data items in response to requests received from clients. The intent of the server is to optimize metrics quantifying the quality of service the system provides. This method of data dissemination has proved to be an efficient means of delivering information in asymmetric environments demanding massive scalability. of critical importance in such a system is the algorithm used by the server to construct a schedule of item broadcasts.;Due to the real-time nature of this problem, performances of heuristics designed to construct such schedules are heavily dependent on request instances. Thus it is challenging to establish the quality of one algorithm over another. Though several scheduling methods have been developed, these algorithms have been studied with a reliance on probabilistic assumptions and little emphasis on analytical results.;In contrast, we provide a formal treatment of the data broadcast scheduling problem in which analytical methods are applied, complemented by simulation experiments. Utilizing a worst-case technique known as competitive analysis, we establish bounds on the performance of various algorithms in the context of several different broadcast models. We describe results in three different settings.;Minimizing the total wait time of all requests with a single channel and multiple database items we establish the competitive ratios for two well-known algorithms, First Come First Served (FCFS) and Most Requests First (MRF) to be equal, and provide a general lower bound for all algorithms in this context. We describe simulation results that indicate the superior performance of MRF over FCFS on average. Minimizing two conflicting metrics, the total wait time and total broadcast cost, with a single channel and single database item we develop two on-line algorithms, establish their competitive ratios, and provide an optimal off-line algorithm used to simulate the impact of various parameters on the performance of both on-line heuristics. Finally, we extend the previous model by including multiple database items and establish a lower bound to a greedy algorithm for this context

Pervasive Data Access in Wireless and Mobile Computing Environments

The rapid advance of wireless and portable computing technology has brought a lot of research interests and momentum to the area of mobile computing. One of the research focus is on pervasive data access. with wireless connections, users can access information at any place at any time. However, various constraints such as limited client capability, limited bandwidth, weak connectivity, and client mobility impose many challenging technical issues. In the past years, tremendous research efforts have been put forth to address the issues related to pervasive data access. A number of interesting research results were reported in the literature. This survey paper reviews important works in two important dimensions of pervasive data access: data broadcast and client caching. In addition, data access techniques aiming at various application requirements (such as time, location, semantics and reliability) are covered

Energy-efficient wireless communication

In this chapter we present an energy-efficient highly adaptive network interface architecture and a novel data link layer protocol for wireless networks that provides Quality of Service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations in bandwidth scheduling and error control are necessary to achieve energy efficiency and an acceptable quality of service. In our approach we apply adaptability through all layers of the protocol stack, and provide feedback to the applications. In this way the applications can adapt the data streams, and the network protocols can adapt the communication parameters