18130193
Preemptive maximum stretch optimization scheduling for wireless on-demand data broadcast
On-demand broadcast is an attractive data dissemination method for mobile and wireless computing. We need an on-demand broadcast scheduling algorithm which can balance individual and overall performance, at the same time avoid the starvation of data items, and scale in terms of client population, database size, and data size in heterogeneous settings. As stretch is regarded as a fair performance metric for variable-sized data requests, in this paper, we propose a new preemptive, heuristic online scheduling algorithm, called PRS for on-demand broadcast system to optimize the worst case stretch across all criteria. We have done a series of simulation experiments to evaluate the performance of our algorithm as compared with other recently proposed methods under a range of scenarios. The experimental results show that our algorithm can substantially reduce the maximum stretch without jeopardizing the overall system performance.
10914298
Wireless real-time on-demand data broadcast scheduling with dual deadlines
In this paper, issues involved in the design of real-time on-demand broadcast system which maintains data temporal constraints are discussed. We propose a new online scheduling algorithm, called RDDS that incorporates access frequency, data size, request-deadline and data-deadline of pending requests for real-time on-demand broadcast system with dual deadlines. Furthermore, the concepts of deferrable requests and non-deferrable requests are introduced, cases of non-deferrable requests are analyzed, and Non-deferrable Request First policy is proposed and integrated into RDDS to form another new algorithm, called RDDS-W. We have performed a series of simulation experiments to evaluate the performance of our algorithms as compared with other previously proposed methods. The experimental results show that our algorithms can substantially outperform other algorithms under a wide range of scenarios, especially when combining with Non-deferrable Request First policy, which improves the performance significantly.
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.
42223547
Pairwise Data Clustering and Applications
Data clustering is an important theoretical topic and a sharp tool for various applications. Its main objective is to partition a given data set into clusters such that the data within the same cluster are "more" similar to each other with respect to certain measures. In this paper, we study the pairwise data clustering problem with pairwise similarity/ dissimilarity measures that need not satisfy the triangle inequality. By using a criterion, called the minimum normalized cut, we model the pairwise data clustering problem as a graph partition problem. The graph partition problem based on minimizing the normalized cut is known to be NP-hard. We present a ((4 + o(1)) ln n)-approximation polynomial time algorithm for the minimum normalized cut problem. We also give a more efficient algorithm for this problem by sacrificing the approximation ratio slightly. Further, our scheme achieves a ((2 + o(1)) ln n)- approximation polynomial time algorithm for computing the sparsest cuts in edge-weighted and vertex-weighted undirected graphs, improving the previously best known approximation ratio by a constant factor.
2385106
The intensity level reduction in radiation therapy
In this paper, we study an interesting intensity level reduction (ILR) problem, which is crucial for solving the "step-and-shoot" delivery planning problem in Intensity-Modulated Radiation Therapy (IMRT). IMRT aims to deliver a highly conformal radiation dose to a target tumor while sparing the surrounding normal tissues. Effective intensity level reduction approaches significantly reduce the patient's exposure under the radiation and improve the treatment efficiency. We propose a new intensity level reduction method for step-and-shoot delivery planning, and further model the intensity level reduction problem as computing a set of shortest monotone paths (SMPs) in certain specific geometric graphs, called multi-column graphs, of pseudo-polynomial sizes. Based on a judicious characterization of the intrinsic geometric structures of the graphs, we develop a novel graph pruning scheme and compute the SMPs without explicitly constructing the graphs, yielding an efficient O(mn logn) time algorithm for solving the intensity level reduction problem on an intensity map (specifying a radiation dose prescription) of size m x n.
13903489
Whole genome phylogeny based on clustered signature string composition
Peptide compositions constructed out of whole sets of protein sequences can be used as species signatures for phylogenetic analysis. To account for point mutations, an amino acid substitution model is integrated into the complete composition vectors through a novel peptide clustering algorithm. Such a refined signature is expected to highlight deeper evolutionary relationships among the species and employed into the whole genome phylogenetic analysis to define a new evolutionary distance measure. Computational experiments have been set up to validate the effectiveness of this new measure and a vertebrate evolutionary tree using a dataset of 832 proteins for 64 vertebrates is reported.
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.
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.