Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1...
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Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.
Technological advances within the area of wireless sensor technology allow WSNs to be used in a increasing number of measurement scenarios. As new application areas are emerging, such as infrastructure monitoring and ...
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Technological advances within the area of wireless sensor technology allow WSNs to be used in a increasing number of measurement scenarios. As new application areas are emerging, such as infrastructure monitoring and smart cities, the need for sensor mobility handling requires efficient and secure authentication protocols. This paper presents EAP-Swift, a novel EAP based authentication protocol with a focus on lightweight processing and faster response. It supports end-to-end session encryption key generation and mutual authentication. By utilizing lightweight hashing algorithms, the challenge-response authentication mechanism uses only two round trips to the AAA server for the complete authentication procedure leading to the reduction of latency by 33% compared to the baseline protocols. Further, using extensive experimentation, we validate that the authentication time can be kept below 250 ms and the power consumption can be kept below 15 mJ. Furthermore, we show that a battery lifetime of more than four years can be achieved when running the system on a regular button cell battery. Finally, the protocol was verified in terms of security using the AVISPA tool.
Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply add...
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Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose using a log-determinant (LogDet) function as a smooth and closer, though nonconvex, approximation to rank for obtaining a low-rank representation in subspace clustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective function on potentially large-scale data. By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering. Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.
This paper proposes a distributed localization algorithm which can be applied to an irregular three-dimensional wireless sensor network, considering the algorithm accuracy and complexity. The algorithm uses clusters t...
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This paper proposes a distributed localization algorithm which can be applied to an irregular three-dimensional wireless sensor network, considering the algorithm accuracy and complexity. The algorithm uses clusters to eliminate the multihop distance errors. An anchor node position optimization scheme is proposed to maximize the uniformity of each subnetwork. The proposed algorithm also employs a new three-dimensional coordinate transformation algorithm, which helps to reduce the errors introduced by coordinate integration between clusters and improves the localization precision. The simulation and performance analysis results show that the localization accuracy of the D3D-MDS algorithm increases by 49.1% compared with 3D-DV-HOP and 38.6% compared with 3D-MDS-MAP. This distributed localization scheme also demonstrates a low computational complexity compared with other centralized localization algorithms.
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In th...
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Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
To effectively handle duplicate files, data deduplication schemes are widely used in many storage systems. Data deduplication algorithms reduce storage space by eliminating data to ensure that only single instance of ...
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To effectively handle duplicate files, data deduplication schemes are widely used in many storage systems. Data deduplication algorithms reduce storage space by eliminating data to ensure that only single instance of data is stored in storage device. In this paper, we propose an energy efficient file synchronization scheme that provides hybrid data chunking using variable-length chunking (VLC) and fixed-length chunking (FLC). The main idea is to analyze similarities between old and new versions of data and decide which chunking method to apply in synchronizing the files. In particular, the proposed algorithm exploits the file similarity pattern for calculating the energy efficiency of chunking algorithms. We have developed an Android mobile application for file synchronization and measured energy consumption. The experiment results show that the proposed scheme helps save energy in synchronizing files, regardless of file types or amount of redundancies the files have.
Recent studies reveal that great benefit can be achieved by employing mobile collectors to gather data in wireless sensor networks. Since the mobile collector can traverse the transmission range of each sensor, the en...
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Recent studies reveal that great benefit can be achieved by employing mobile collectors to gather data in wireless sensor networks. Since the mobile collector can traverse the transmission range of each sensor, the energy of nodes may be saved near maximally. However, for directly receiving data packet from every node, the length of mobile collector route should be very long. Hence it may significantly increase the data gathering latency. To solve this problem, several algorithms have been proposed. One of them called BRH-MDG found that data gathering latency can be effectively shortened by performing proper local aggregation via multihop transmissions and then uploading the aggregated data to the mobile collector. But, the BRH-MDG algorithm did not carefully analyze and optimize the energy consumption of the entire network. In this paper, we propose a mathematical model for the energy consumption of the LNs and present a new algorithm called EEBRHM. The simulation results show that under the premise of bounded relay hop, compared with BRH-MDG, EEBRHM can prolong the networks lifetime by 730%.
Based on multiobjective particle swarm optimization, a localization algorithm named multiobjective particle swarm optimization localization algorithm (MOPSOLA) is proposed to solve the multiobjective optimization loca...
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Based on multiobjective particle swarm optimization, a localization algorithm named multiobjective particle swarm optimization localization algorithm (MOPSOLA) is proposed to solve the multiobjective optimization localization issues in wireless sensor networks. The multiobjective functions consist of the space distance constraint and the geometric topology constraint. The optimal solution is found by multiobjective particle swarm optimization algorithm. Dynamic method is adopted to maintain the archive in order to limit the size of archive, and the global optimum is obtained according to the proportion of selection. The simulation results show considerable improvements in terms of localization accuracy and convergence rate while keeping a limited archive size by a method using the global optimal selection operator and dynamically maintaining the archive.
Resource allocation is expected to be amost important factor especially for heterogeneous applications in wireless ad hoc networks. In this paper, a novel heterogeneous resource allocation algorithm (HRA) is presented...
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Resource allocation is expected to be amost important factor especially for heterogeneous applications in wireless ad hoc networks. In this paper, a novel heterogeneous resource allocation algorithm (HRA) is presented for ad hoc networks, supporting both elastic and inelastic traffic. First, by combining the first order Lagrangian method with pseudo utility, the original nonconvex problem is converted into a new convex one. Then, we successfully solve the heterogeneous problem with the dual-based decomposition approach. In addition, we integrate utility fairness into the resource allocation framework, which can adaptively manage the tradeoff between elastic and inelastic flows. Simulations show and prove that HRA converges fast and can achieve the global optimum starting from many different network conditions, such as elastic, inelastic, and hybrid scenario. With both considerations of flow rate and utility fairness, HRA improves the overall network utility and system throughput greatly.
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