Wireless sensor networks (WSN) is composed of so many small sensor nodes which have limited resources. So the technique that raises energy efficiency is the key to prolong the network life time. In the paper, we propose an agent based framework which takes the biological characteristics of gene. The gene represents an operation policy to control agent behavior. Agents are aggregated to reduce duplicate transmissions in active period. And it selects next hop based on the information of neighbor agents. Among neighbors, the node which has enough energy is given higher priority. The base station processes genetic evolution to refine the behavior policy of agent. Each agent is taken latest gene and spread recursively to find the optimal gene. Our proposed framework yields sensor nodes that have the properties of self-healing, self-configuration, and self-optimization. Simulation results show that our proposed framework increases the lifetime of each node.

Although the Marching Cube (MC) algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC algorithm considers a group of voxels simultaneously for the application of the MC algorithm to explore interesting features of the original MC algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC algorithm is 3 to more than 10 times faster than the algorithm using a hash table. Another significant advantage of GMC is its compatibility with other algorithms that accelerate the MC algorithm. Together, the overall performance of the original MC algorithm is promoted even further.

A new compact form of the sliding window recursive least squares (SWRLS) algorithm, the I-SWRLS algorithm, is derived using an indefinite matrix. The resultant algorithm has a form similar to that of the traditional recursive least squares (RLS) algorithm, and is more computationally efficient than the conventional SWRLS algorithm including two Riccati equations. Furthermore, a computationally reduced version of the I-SWRLS algorithm is developed utilizing a shift property of the correlation matrix of input data. The resulting fast algorithm reduces the computational complexity from O(N2) to O(N) per iteration when the filter length (tap number) is N, but retains the same tracking performance as the original algorithm. This fast algorithm is much easier to implement than the existing SWC FTF algorithms.

In ubiquitous sensor networks, extra energy savings can be achieved by selecting the filtering solution to counter the attack. This adaptive selection process employs a fuzzy rule-based system for selecting the best solution, as there is uncertainty in the reasoning processes as well as imprecision in the data. In order to maximize the performance of the fuzzy system the membership functions should be optimized. However, the efforts required to perform this optimization manually can be impractical for commonly used applications. This paper presents a GA-based membership function optimizer for fuzzy adaptive filtering (GAOFF) in ubiquitous sensor networks, in which the efficiency of the membership functions is measured based on simulation results and optimized by GA. The proposed optimization consists of three units; the first performs a simulation using a set of membership functions, the second evaluates the performance of the membership functions based on the simulation results, and the third constructs a population representing the membership functions by GA. The proposed method can optimize the membership functions automatically while utilizing minimal human expertise.

Although a large number of query processing algorithms in spatial network database (SNDB) have been studied, there exists little research on route-based queries. Since moving objects move only in spatial networks, route-based queries, like in-route nearest neighbor (IRNN), are essential for Location-based Service (LBS) and Telematics applications. However, the existing IRNN query processing algorithm has a problem in that it does not consider time and space constraints. Therefore, we, in this paper, propose IRNN query processing algorithms which take both time and space constraints into consideration. Finally, we show the effectiveness of our IRNN query processing algorithms considering time and space constraints by comparing them with the existing IRNN algorithm.

We introduce a “generalized small inverse problem (GSIP)” and present an algorithm for solving this problem. GSIP is formulated as finding small solutions of f(x0, x1, ..., xn)=x0 h(x1, ..., xn)+C=0 (mod ; M) for an n-variate polynomial h, non-zero integers C and M. Our algorithm is based on lattice-based Coppersmith technique. We provide a strategy for construction of a lattice basis for solving f=0, which is systematically transformed from a lattice basis for solving h=0. Then, we derive an upper bound such that the target problem can be solved in polynomial time in log M in an explicit form. Since GSIPs include some RSA-related problems, our algorithm is applicable to them. For example, the small key attacks by Boneh and Durfee are re-found automatically.

In this paper, a new algorithm called TGA is introduced which defines the concept of time more naturally for the first time. A parameter called TimeToLive is considered for each chromosome, which is a time duration in which it could participate in the process of the algorithm. This will lead to keeping the dynamism of algorithm in addition to maintaining its convergence sufficiently and stably. Thus, the TGA guarantees not to result in premature convergence or stagnation providing necessary convergence to achieve optimal answer. Moreover, the mutation operator is used more meaningfully in the TGA. Mutation probability has direct relation with parent similarity. This kind of mutation will decrease ineffective mating percent which does not make any improvement in offspring individuals and also it is more natural. Simulation results show that one run of the TGA is enough to reach the optimum answer and the TGA outperforms the standard genetic algorithm.

This paper presents a simplifying method of the two previous fault attacks to pairing and the Miller algorithms based on a practical fault assumption. Our experimental result shows that the assumption is feasible and easy to implement.

This paper develops planar circuit filters consisting of arbitrarily-shaped conductor patches and slots on a conductor-backed dielectric substrate, which are designed by an optimization technique based on the genetic algorithm. The developed filter has multiple resonators and their mutual couplings in the limited space by using both sides of the substrate, so that its compactness is realized. We first demonstrate the effectiveness of the present filter structure from some design samples numerically and experimentally. Then as a practical application, we design compact UWB filters, and their filter characteristics are verified from the measurements.

This paper presents a new and robust framework for real-coded genetic algorithm, called real-code conditional genetic algorithm (rc-CGA). The most important characteristic of the proposed rc-CGA is the implicit self-adaptive feature of the crossover and mutation mechanism. Besides, a new crossover operator with laplace distribution following a few promising descent directions (FPDD-LX) is proposed for the rc-CGA. The proposed genetic algorithm (rc-CGA+FPDD-LX) is tested using 31 benchmark functions and compared with four existing algorithms. The simulation results show excellent performance of the proposed rc-CGA+FPDD-LX for continuous function optimization.

In an undirected graph, the feedback vertex set (FVS for short) problem is to find a set of vertices of minimum cardinality whose removal makes the graph acyclic. The FVS has applications to several areas such that combinatorial circuit design, synchronous systems, computer systems, VLSI circuits and so on. The FVS problem is known to be NP-hard on general graphs but interesting polynomial solutions have been found for some special classes of graphs. In this paper, we present an O(n2.68 + γn) time algorithm for solving the FVS problem on trapezoid graphs, where γ is the total number of factors included in all maximal cliques.

In the case of Barreto-Naehrig pairing-friendly curves of embedding degree 12 of order r, recent efficient Ate pairings such as R-ate, optimal, and Xate pairings achieve Miller loop lengths of(1/4) ⌊log2 r⌋. On the other hand, the twisted Ate pairing requires (3/4) ⌊log2 r⌋ loop iterations, and thus is usually slower than the recent efficient Ate pairings. This paper proposes an improved twisted Ate pairing using Frobenius maps and a small scalar multiplication. The proposed idea splits the Miller's algorithm calculation into several independent parts, for which multi-pairing techniques apply efficiently. The maximum number of loop iterations in Miller's algorithm for the proposed twisted Ate pairing is equal to the (1/4) ⌊log2 r ⌋ attained by the most efficient Ate pairings.

When a link or node fails in a network, the affected flows are automatically rerouted. This increases the hop counts of the flows, which can drastically degrade network performance. Keeping the hop lengths as stable as possible, i.e., minimizing the difference in hop length between the original flow and the rerouted flow is important for network reliability. Therefore, network service providers need a method for designing networks that stabilizes the flow hop length and maintains connectivity during a link or node failure with limited investment cost. First, we formulate the network design problem used for determining the set of links to be added that satisfies the required constraints on flow hop length stability, connectivity, and node degree. Next, we prove that this problem is NP-complete and present two approximation algorithms for the optimization problem so as to minimize the number of links added. Evaluation of the performance of these algorithms by using 39 backbone networks of commercial ISPs and networks generated by two well-known models showed that the proposed algorithms provide effective solutions in sufficiently short computation time.

In this paper, we propose a new interference alignment (IA) scheme that jointly designs the linear transmitter and receiver for the 2-user MIMO X channel system, using minimum total mean square error criterion, subject to each transmitter power constraint. We show that transmitters and receivers under such criteria could be realized through a joint iterative algorithm. Considering the imperfection of channel state information (CSI), we also extend the minimum mean square error interference alignment schemes for the MIMO X channel with CSI estimation error. A robust iterative algorithm which is insensitve to CSI estimation error is proposed. Simulation results are also provided to demonstrate the proposed algorithm.

Hop-timing detection is of extreme importance for the reception of frequency hopping (FH) signals. Any error in the hop-timing detection has a deleterious effect on the performance of the receiver in frequency hopping (FH) communication systems. However, it is not easy to detect the hop-timing under low signal to noise power ratio (SNR) environments. Adaptive array antennas (AAA) have been expected to improve the performance of FH communication systems by beamforming for the direction of arrival of the desired signal. Since the conventional AAA exploits at least the coarse synchronization for dehopping of FH signals before achieving the beamforming, any fault in the hop-timing detection causes the deterioration of the performance of AAA. Using AAA based on the constant modulus algorithm (CMA), this paper proposes a new method for blind beamforming and hop-timing detection for FH signals. The proposed method exploits both the spatial and temporal characteristics of the received signal to accomplish the beamforming and detect the hop-timing without knowing any a priori information such as fine/coarse time synchronization and training signal. The performance verifications of the proposed method based on pertinent simulations are presented.

Recent works on MIMO receiver design were mainly focused on sphere decoding, which provides a trade-off between the performance and complexity by suitably choosing the “radius” or the number of candidates in the search space. Meanwhile, another approach, called poly-diagonalization and trellis detection, has been proposed to compromise the complexity and performance. In this paper, we compare various MIMO receiver algorithms in terms of both performance and complexity. The performance is evaluated in a frequency selective fading channel environment on the basis of orthogonal frequency division multiplexing with channel coding, for which the generation of soft decision values is crucial. The simulations show that the poly-diagonalization approach matches the performance of sphere decoding at similar computational complexity.

Network on Chip (NoC) is proposed as a new intra-chip communication infrastructure. In current NoC design, one related problem is mapping IP cores onto NoC architectures. In this paper, we propose a performance-aware hybrid algorithm (PHA) for mesh-based NoC to optimize performance indexes such as latency, energy consumption and maximal link bandwidth. The PHA is a hybrid algorithm, which integrates the advantages of Greedy Algorithm, Genetic Algorithm and Simulated Annealing Algorithm. In the PHA, there are three features. First, it generates a fine initial population efficiently in a greedy swap way. Second, effective global parallel search is implemented by genetic operations such as crossover and mutation, which are implemented with adaptive probabilities according to the diversity of population. Third, probabilistic acceptance of a worse solution using simulated annealing method greatly improves the performance of local search. Compared with several previous mapping algorithms such as MOGA and TGA, simulation results show that our algorithm enhances the performance by 30.7%, 23.1% and 25.2% in energy consumption, latency and maximal link bandwidth respectively. Moreover, simulation results demonstrate that our PHA approach has the highest convergence speed among the three algorithms. These results show that our proposed mapping algorithm is more effective and efficient.

Vehicle routing is an important combinatorial optimization problem. In real transport networks,the travel speed and travel time of roads have large time-variability and randomness. The study of vehicle routing problem in time-dependent network has even more practical value than static network VRP problem. This paper combines the features of time-dependent networks and gives the mathematical models of the time-dependent vehicle routing problem. On this basis, the traditional ant colony optimization algorithm is improved. A new path transfer strategy of ants and new dynamic pheromone update strategy applicable to time-dependent network are proposed. Based on these strategies, the improved ant colony algorithm is given for solving the vehicle routing problem in time-dependent networks. The simulation results show that the algorithm can effectively solve the vehicle routing problem in time-dependent network and has better computational efficiency and convergence speed.

A zig-zag Gardner algorithm with parallel architecture is presented in this letter. This algorithm performs timing adjustment in each individual burst independently for high speed wireless burst communication with a short guard. Over sampling data are stored in RAM initially and read forward and backward alternately later. The proposed algorithm has distinct symmetric characteristic in the forward and backward process, which makes the alternate sequences achieve nearly the same effect as a continuous sequence. The performance of the proposed algorithm is very close to the theoretical curve.

The aggregate throughput of wireless mesh networks (WMNs) can be significantly improved by equipping the mesh routers with multiple radios tuned to orthogonal channels. Not only the links using orthogonal channels can be activated at a time, but some links in the same channel also can be activated concurrently if the Signal-to-Interference-and-Noise Ratio (SINR) at their receivers is not lower than the threshold, which is the spatial-reuse characteristic. STDMA is considered as one of the medium access schemes that can exploit spatial reuse to improve network throughput. Past studies have shown that optimizing the performance of STDMA is NP-Hard. Therefore, we propose a STDMA-based scheduling algorithm that operates in a greedy fashion for WMNs. We show that the proposed algorithm enhances not only the throughput but also the fairness by capturing the essence of spatial-reuse approach of STDMA and giving medium access opportunities to each network element based on its priority. We furthermore validate our algorithm through theoretical analysis and extensive simulations and the results show that our algorithm can outperform state-of-the-art alternatives.