16765511
A preemptive scheduling algorithm for wireless real-time on-demand data broadcast
On-demand broadcast is an attractive data dissemination method for mobile and wireless computing. In this paper, we propose a new online preemptive scheduling algorithm, called PRDS that incorporates the urgency, the data size and the number of pending requests for real-time on-demand broadcast system. Furthermore, we use pyramid preemption to optimize performance and reduce overhead. We have done a series of simulation experiments to evaluate the performance of our algorithm as compared with other previously proposed methods under a range of scenarios. The experimental results show that our algorithm can substantially outperform other algorithms without jeopardizing other performance metrics, such as response time and stretch.
19709239
Scheduling real-time requests in on-demand data broadcast environments
On-demand broadcast is an attractive data dissemination method for mobile and wireless computing. In this paper, we propose a new online preemptive scheduling algorithm, called PRDS that incorporates urgency, data size and number of pending requests for real-time on-demand broadcast system. Furthermore, we use pyramid preemption to optimize performance and reduce overhead. A series of simulation experiments have been performed to evaluate the real-time performance of our algorithm as compared with other previously proposed methods. The experimental results show that our algorithm substantially outperforms other algorithms over a wide range of workloads and parameter settings.
14353526
A logical framework for identifying quality knowledge from different data sources
As the Web has emerged as a large distributed data repository, individuals and organizations have been able to utilize the low-cost information and knowledge on the Internet when making business decisions. Because data in different data sources may be conflictive or untrue, researchers and practitioners must intensify efforts to develop appropriate techniques for its efficient use and management. In this paper, a logical framework is designed for identifying quality knowledge from different data sources, thus working towards the development of an agreed ontology. Our experimental results have demonstrated that the approach is promising, and that a minor data enhancement adjustment could bring higher effectiveness.
6198928
Energy-Efficient Deployment of Mobile Sensor Networks by PSO
Sensor deployment is an important issue in designing sensor networks. In this paper, particle swarm optimization (PSO) approach is applied to maximize the coverage based on a probabilistic sensor model in mobile sensor networks and to reduce cost by finding the optimal positions for the cluster-head nodes based on a well-known energy model. During the coverage optimization process, sensors move to form a uniformly distributed topology according to the execution of algorithm at base station. The simulation results show that PSO algorithm has faster convergence rate than genetic algorithm based method while achieving the goal of energy efficient sensor deployment.
7782106
Localized Energy-Aware Broadcast Protocol for Wireless Networks with Directional Antennas
We consider broadcast protocols in wireless networks that have limited energy and computation resources. The well-known algorithm, DBIP (Directional Broadcast Incremental Power), which exploits “Incremental Power” philosophy for wireless networks with directional antenna to construct broadcasting tree, provides very good results in terms of energy savings. Unfortunately, its computation is centralized, as the source node needs to know the entire topology of the network. Mobility of nodes or frequent changes in the node activity status (from “active” to “passive” and vice-versa) may cause global changes in topology which must be propagated throughout the network for any centralized solution. This may results in extreme and un-acceptable communication overhead. In this paper, we propose and evaluate a localized energy-efficient broadcast protocol, Localized Directional Broadcast Incremental Power Protocol (LDBIP), which employs distributed location information and computation to construct broadcast trees. In the proposed method, a source node sets up spanning tree with its local neighborhood position information and includes certain hops relay information in packet. Directional antennas are used for transmitting broadcast packet, and the transmission power is adjusted for each transmission to the minimal necessary for reaching the particular neighbor. Relay nodes will consider relay instructions received to compute their own local neighborhood spanning tree and then rebroadcasts. Experimental results verify that this new protocol shows similar performance with DBIP in static wireless networks, and better performance in mobile scenarios.
36354030
Globally optimal uneven error-protected packetization of scalable code streams
In this paper, we present a family of new algorithms for rate-fidelity optimal packetization of scalable source bit streams with uneven error protection. In the most general setting where no assumption is made on the probability function of packet loss or on the rate-fidelity function of the scalable code stream, one of our algorithms can find the globally optimal solution to the problem in O(N/sup 2/L/sup 2/) time, compared to a previously obtained O(N/sup 3/L/sup 2/) complexity, where N is the number of packets and L is the packet payload size. If the rate-fidelity function of the input is convex, the time complexity can be reduced to O(NL/sup 2/) for a class of erasure channels, including channels for which the probability function of losing n packets is monotonically decreasing in n and independent erasure channels with packet erasure rate no larger than N/2(N + 1). Furthermore, our O(NL/sup 2/) algorithm for the convex case can be modified to rind an approximation solution for the general case. All of our algorithms do away with the expediency of fractional bit allocation, a limitation of some existing algorithms.
33348508
Knowledge Discovery in Multiple Databases