In this letter, we propose three algorithms to reduce congestion for greedy forwarding, which is one of common principles in geographic routing. The new algorithms take geographic position information and network congestion metrics to balance traffic. When these algorithms are combined with well-known GPSR protocol [1], packet delivery ratio is enhanced by reducing number of lost packets in a buffer. In addition, end-to-end delay is reduced by bypassing congested nodes. These features are evaluated and analyzed through several simulation results.

In this letter we propose a novel resource allocation and admission control strategy for OFDMA-based emerging LTE systems. Considering users' reneging and migration between service providers, we first prove that the optimal resource allocation problem, which maximizes the service provider's gross income is, NP-complete. Subsequently, we propose two different heuristics based on dynamic programming and greedy algorithms to get a near-optimal resource allocation and admission control strategy in computationally feasible time. Simulation results point out that the solutions offer increased gross income of the service provider, while offering low latency, adequate throughput and session acceptance.

Recently multi-car elevator (MCE) consisting of several elevator cars in a single elevator shaft received great interest as transportation systems for high-rise buildings. Algorithms for efficiently controlling elevator cars are necessary to put MCEs to practical use. We propose an algorithm for controlling MCEs to reduce passenger-waiting time. A feature of our algorithm is the introduction of a simple function estimating efficiency of passenger transport and a procedure checking assignability of a car. We evaluate the performance of our algorithm using a simulation and show that it performs better compared with a previous algorithm.

A data stream is a series of massive unbounded tuples continuously generated at a rapid rate. Continuous queries for data streams should be processed continuously, so that a strict time constraint is required. In most previous research studies, in order to guarantee this constraint, the evaluation order of join predicates in a continuous query is optimized using a greedy strategy. However, because a greedy strategy traces only the first promising plan, it often finds a suboptimal plan. To reduce the possibility of producing a suboptimal plan, in this paper, we propose an improved scheme, k-Extended Greedy Algorithm (k-EGA), that simultaneously examines a set of promising plans and reoptimize an execution plan adaptively. The number of promising plans is flexibly controlled by a user-defined range variable. The scheme verifies the performance of the current plan periodically. If the plan is no longer efficient, a newly optimized plan is generated. The performance of the proposed scheme is verified through various experiments to identify its various characteristics.

An efficient way to develop large scale speech corpora is to collect phonetically rich ones that have high coverage of phonetic contextual units. The sentence set, usually called as the minimum set, should have small text size in order to reduce the collection cost. It can be selected by a greedy search algorithm from a large mother text corpus. With the inclusion of more and more phonetic contextual effects, the number of different phonetic contextual units increased dramatically, making the search not a trivial issue. In order to improve the search efficiency, we previously proposed a so-called least-to-most-ordered greedy search based on the conventional algorithms. This paper evaluated these algorithms in order to show their different characteristics. The experimental results showed that the least-to-most-ordered methods successfully achieved smaller objective sets at significantly less computation time, when compared with the conventional ones. This algorithm has already been applied to the development a number of speech corpora, including a large scale phonetically rich Chinese speech corpus ATRPTH which played an important role in developing our multi-language translation system.

This paper presents a motion-based boundary tracking method for a moving deformable object in an image sequence using a parametric active contour model. Deciding the local converging directions of the contour points is essential for correctly extracting the boundary of a moving deformable object. Thus, a new energy function for a parametric active contour model is proposed based on the addition of a directional energy term using a frame difference map to the greedy snake. The frame difference map is used to obtain motion information on an object with fast and non-rigid motion. Plus, updating rules for the frame difference map are also developed to encourage the stable convergence of the contour points. Experiments on a set of synthetic and real image sequences show that the proposed method could fully track a speedy deformable object while exactly extracting the boundary of the object in every frame.

In most research work for sensor network routings, perfect aggregation has been assumed. Such an assumption might limit the application of the wireless sensor networks. We address the impact of aggregation efficiency on energy consumption in the context of GIT routing. Our questions are how the most efficient aggregation point changes according to aggregation efficiency and the extent to which energy consumption can decrease compared to the original GIT routing and opportunistic routing. To answer these questions, we analyze a two-source model, which yields results that lend insight into the impact of aggregation efficiency. Based on analytical results, we propose an improved GIT: "aggregation efficiency-aware GIT," or AGIT. We also consider a suppression scheme for exploratory messages: "hop exploratory." Our simulation results show that the AGIT routing saves the energy consumption of the data transmission compared to the original GIT routing and opportunistic routing.

Receiver enhancement at mobile terminals such as using receiver diversity is a way of achieving greater downlink capacity. The enhancement, however, is achieved not instantaneously by a network operator but gradually by the individual users that choose and purchase their own mobile terminals. We investigate in this letter the effect of gradually introducing enhanced receivers at mobiles in different locations. With greedy scheduling, capacity, fairness and coverage are quantified and numerically compared according to locations of enhanced mobiles. The results show that the enhancement made at mobiles nearer to the base provides the greater capacity but this capacity-driving introduction of the enhancement makes the fairness and the coverage poorer.

The WDM optical networks currently being deployed are opaque optical networks, in which each link is optically isolated by transponders. To reduce the number of expensive transponders and switching ports, a hierarchical optical architecture consisting of all-optical waveband switching and opaque OEO switching has been proposed. Although this architecture requires fewer transponders and ports, it also requires a large number of wavelength (waveband) multiplexers and demultiplexers. Switching the optical path solely at the fiber level (i.e., by using fiber cross-connects, or FXCs) is desirable as a way to reduce the total node cost. If all the core nodes in an optical network are FXCs, however, the grooming of wavelengths for the optical fibers is only possible at the edge nodes. This leads to poor utilization of wavelength resources when there is only demand for small numbers of wavelengths, and as a result, the link cost increases. This problem can be solved by adding an OEO grooming function to some of the FXCs. In this paper, we propose an algorithm for designing optical cross-connect (OXC) functions on the basis of the FXC, thus minimizing the total network cost.

Load balancing among multiple mirror servers located at distributed positions in the network is a key technique for content delivery services. For bandwidth allocated services, we consider how to select a suitable server from several candidates containing the same content at the time of a request. We propose limiting the candidates in advance and selecting a server from the limited set of servers in a round-robin fashion. The server sets that minimize the variance of the link load are derived using a greedy method for a given network topology and service demand. Through numerical evaluation, we demonstrate that the proposed method is superior to previous methods.

A dual greedy polyhedron is defined by a system of linear inequalities, where the right-hand sides are given by a submodular function and the coefficients matrix is given by the incidence vectors of antichains of a rooted forest. Faigle and Kern introduced this concept and showed that a dual greedy algorithm works for the linear program over dual greedy polyhedra. In this paper, we show that a dual greedy polyhedron is the isomorphic image of an ordinary submodular polyhedron under the Mobius function of the underlying rooted forest. This observation enables us to reduce linear optimization problems over dual greedy polyhedra to those over ordinary submodular polyhedra. We show a new max-min theorem for intersection of two dual greedy polyhedra as well.

The main problem considered is submodular set cover, the problem of minimizing a linear function under a nondecreasing submodular constraint, which generalizes both well-known set cover and minimum matroid base problems. The problem is NP-hard, and two natural greedy heuristics are introduced along with analysis of their performance. As applications of these heuristics we consider various special cases of submodular set cover, including partial cover variants of set cover and vertex cover, and node-deletion problems for hereditary and matroidal properties. An approximation bound derived for each of them is either matching or generalizing the best existing bounds.

We study some sufficient conditions of codeword lengths for the existence of a fix-free code. Ahlswede et al. proposed the 3/4 conjecture that Σi=1n a-li 3/4 implies the existence of a fix-free code with lengths li when a=2 i. e. the alphabet is binary. We propose a more general conjecture, and prove that the upper bound of our conjecture is not greater than 3/4 for any finite alphabet. Moreover, we show that for any a2 our conjecture is true if codeword lengths l1,l2,. . . consist of only two kinds of lengths.

A novel combinatorial optimization algorithm called 2-stage discrete optimization method (2DOM) is proposed for the largest common subgraph problem (LCSP) in this paper. Given two graphs G=(V1, E1) and H=(V2, E2), the goal of LCSP is to find a subgraph G'=(V1', E1') of G and a subgraph H'=(V2', E2') of H such that G' and H' are not only isomorphic to each other but also their number of edges is maximized. The two graphs G' and H' are isomorphic when |V1'|=|V2'| and |E1'|=|E2'|, and there exists one-to-one vertex correspondence f: V1' V2' such that {u, v} E1' if and only if{f(u), f(v)} E2'. LCSP is known to be NP-complete in general. The 2DOM consists of a construction stage and a refinement stage to achieve the high solution quality and the short computation time for large size difficult combinatorial optimization problems. The construction stage creates a feasible initial solution with considerable quality, based on a greedy heuristic method. The refinement stage improves it keeping the feasibility, based on a random discrete descent method. The performance is evaluated by solving two types of randomly generated 1200 LCSP instances with a maximum of 500 vertices for G and 1000 vertices for H. The simulation result shows the superiority of 2DOM to the simulated annealing in terms of the solution quality and the computation time.

In this paper, a new multicast routing method for layered streams is proposed. This method is an extension of the weighted greedy algorithm (WGA) and uses two kinds of weight values to refine the link distance. It can cope with dynamic change in the group members without multicast tree re-construction. The method is compatible with the RSVP and can be utilized in existing shared tree type routing protocols such as CBT and PIM sparse mode. The network resources can be utilized efficiently; furthermore, the loss rate of member's requests to receive more layers can be reduced by this routing method when a sufficient number of nodes have the packet filtering function and a sufficient number of hops is permitted.

The mapping from a global routing to a feasible detailed routing in a number of 2D array routing structures has been shown to be an NP-complete problem. These routing structures include the Xilinx style routing architecture, as well as architectures with significantly higher switching flexibility. In response to this complexity, a different class of FPGA routing structures called Greedy Routing Architectures (GRAs) have been proposed. On GRAs, optimally routing each switch box, in a specified order, leads to an optimal chip routing. Because routing each switch box takes polynomial time, the mapping problem on GRAs can be solved in polynomial time. In particular, an H-tree GRA with W2+2W switches per switch box (SpSB) and a 2D array GRA with 4W2+2W SpSB have been proposed. In this paper, we improve on these results by introducing an H-tree GRA with W2/2+2W SpSB and a 2D array GRA with 3.5W2+2W SpSB. These new GRAs have the same desirable mapping properties of the previously described GRAs, but use fewer switches.

An approximation algorithm composed of a digital neural network (DNN) and a modified greedy algorithm (MGA) is presented for the board-level routing problem (BLRP) in a logic emulation system based on field-programmable gate arrays (FPGA's) in this paper. For a rapid prototyping of large scale digital systems, multiple FPGA's provide an efficient logic emulation system, where signals or nets between design partitions embedded on different FPGA's are connected through crossbars. The goal of BLRP, known to be NP-complete in general, is to find a net assignment to crossbars subject to the constraint that all the terminals of any net must be connected through a single crossbar while the number of I/O pins designated for each crossbar m is limited in an FPGA. In the proposed combination algorithm, DNN is applied for m = 1 and MGA is for m 2 in order to achieve the high solution quality. The DNN for the N-net-M-crossbar BLRP consists of N M digital neurons of binary outputs and range-limited non-negative integer inputs with integer parameters. The MGA is modified from the algorithm by Lin et al. The performance is verified through massive simulations, where our algorithm drastically improves the routing capability over the latest greedy algorithms.