Wireless Sensor Networks(WSNs) has become a popular research topic due to its resource constraints. Energy consumption and transmission delay is crucial requirement to be handled to enhance the popularity of WSNs. In ...
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Wireless Sensor Networks(WSNs) has become a popular research topic due to its resource constraints. Energy consumption and transmission delay is crucial requirement to be handled to enhance the popularity of WSNs. In order to overcome these issues, we have proposed an Efficient Packet Scheduling Technique for Data Merging in WSNs. Packet scheduling is done by using three levels of priority queue and to reduce the transmission delay. Real-time data packets are placed in high priority queue and Non real-time data packets based on local or remote data are placed on other queues. In this paper, we have used Time Division Multiple Access(TDMA) scheme to efficiently determine the priority of the packet at each level and transmit the data packets from lower level to higher level through intermediate nodes. To reduce the number of transmission, efficient data merge technique is used to merge the data packet in intermediate nodes which has same destination node. Data merge utilize the maximum packet size by appending the merged packets with received packets till the maximum packet size or maximum waiting time is reached. Real-time data packets are directly forwarded to the next node without applying data merge. The performance is evaluated under various metrics like packet delivery ratio, packet drop, energy consumption and delay based on changing the number of nodes and transmission rate. Our results show significant reduction in various performance metrics.
If the glowworm individual has no memory during its movement, and the decision of next direction is limited to its current position. It is precisely these reasons mentioned above that make the basic Glowworm Swarm Opt...
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If the glowworm individual has no memory during its movement, and the decision of next direction is limited to its current position. It is precisely these reasons mentioned above that make the basic Glowworm Swarm Optimization easy to trap into the local optimum. In order to solve the problem, this paper suggests a Shuffled mutation glowworm swarm optimization(SMGSO), which combines the thought of Shuffled Frog Leaping with Glowworm Swarm Optimization. Making use of a grouping idea of Shuffled Mutation, the glowworm swarm is divided into several subgroups. The location updating of each individual is not only influenced by the brightest node in neighbour scope, but also by the brightest one in their local subgroup, meanwhile the locations of those isolated nodes are updated by the difference mutation of the global optimum and local optimal. In group shuffling stage, an orthogonal strategy can guide the whole population to generate their offspring. The performance of this proposed approach is examined by well-known10 benchmark functions, and its obtained results are compared with what other variants hold. The experimental analysis show that the Shuffled mutation glowworm swarm optimization is effective and outperforms other variants in terms of solving multi-modal function optimization problems, and the proposed approach can improve the positioning accuracy of the centroid localization.
The existing edge routers can not assign the link capacities between User datagram protocol(UDP) subscribers and Transmission control protocol(TCP) subscribers to ensure the multiple priorities traffics with Different...
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The existing edge routers can not assign the link capacities between User datagram protocol(UDP) subscribers and Transmission control protocol(TCP) subscribers to ensure the multiple priorities traffics with Differentiate-Serve(Diff Serv). To solve the problem,a new Diff Serv edge router with Controlled-UDP(C-UDP)is proposed. The proposed Diff Serv edge router can control the data rates of UDP and TCP subscribers according to their priorities. In the proposed edge router, there are multi-queues buffers controlled by TCP Active queue management(AQM) and UDP AQM algorithms to implement fair and stable link capacities. The proposed TCP AQM and UDP AQM algorithms achieve the network congestion control and Diff Serv by operating AQM parameters on the stable conditions proposed by us. The dynamic simulation results demonstrate the proposed edge router for Diff Serv network to be valid.
Methods for the standardization of a manufacturing information system (MIS) software interface are put forward in this paper, the interface is described and test methods are raised, and CAD system application interfac...
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An automatic crop disease recognition method was proposed in this paper,which combined the statistical features of leaf images and meteorological *** images of infected crop leaves were taken under different environme...
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An automatic crop disease recognition method was proposed in this paper,which combined the statistical features of leaf images and meteorological *** images of infected crop leaves were taken under different environments of the growth periods,temperature and *** methods of image morphological operation,contour extraction and region growing algorithm were adopted for leaf image enhancement and spot image *** each image of infected crop leaf,the statistical features of color,texture and shape were extracted by image processing,and the optimal meteorological features with the highest accuracy rate were obtained and selected by the attribute reduction *** fusion feature vector of the image was formed by combining the statistical features and the meteorological *** the probabilistic neural networks(PNNs)classifier was adopted to evaluate the classification *** experimental results on three cucumber diseased leaf image datasets,i.e.,downy mildew,blight and anthracnose,showed that the crop diseases can be effectively recognized by the integrated application of leaf image processing technology,the disease meteorological data and PNNs classifier,and the recognition accuracy rate was higher than 90%,which indicated that the PNNs classifier trained on the disease feature coefficients extracted from the crop disease leaves and meteorological data could achieve higher classification accuracy.
Predicting the three-dimensional structure of proteins from amino acid sequences with only a few remote homologs,or de novo prediction,remains a major challenge in computational *** modeling of the protein backbone re...
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Predicting the three-dimensional structure of proteins from amino acid sequences with only a few remote homologs,or de novo prediction,remains a major challenge in computational *** modeling of the protein backbone represents the initial phase of a protein structure prediction *** a parallel ant colony optimization based on sharing one pheromone matrix,this report proposes a parallel approach to predict the structure of a protein *** parallel approach combines various sources of energy functions and generates protein backbones with the lowest energies jointly determined by the various energy *** free modeling targets in CASP8/9 are used to evaluate the performance of the *** 13 targets in CASP8,two out of the predicted model1s selected by our approach are the best of the published CASP8 results,and seven out of the model1s are ranked in the top *** 29 targets in CASP9,20 out of the best models from our predictions are ranked in the top 10,and 11 out of the model1s are ranked in the top 10.
This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then...
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This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then the global controller abstracts substrate network topology based on the candidate nodes and boundary nodes of each domain, and applies Particle Swarm Optimization Algorithm on it to divide virtual network requests. Each local controller then embeds the virtual nodes of the divided single-domain virtual network requests in the domain, and cooperates with other local controllers to embed the inter-domain virtual links. Simulation experimental results show that the proposed algorithm has good performance in reducing embedding cost with good stability and scalability.
Dear editor,Reduction of finite automata (FA) is of great importance because of its practical applications in engineering; for example the memory space of hardware realization grows exponentially with the number of st...
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Dear editor,Reduction of finite automata (FA) is of great importance because of its practical applications in engineering; for example the memory space of hardware realization grows exponentially with the number of states of FSMs. Existing results for reducing FA can roughly be classified into four categories:merging of states [1], refining of the state
Cloud-based storage is a service model for businesses and individual users that involves paid or free storage resources. This service model enables on-demand storage capacity and management to users anywhere via the I...
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Cloud-based storage is a service model for businesses and individual users that involves paid or free storage resources. This service model enables on-demand storage capacity and management to users anywhere via the Internet. Because most cloud storage is provided by third-party service providers, the trust required for the cloud storage providers and the shared multi-tenant environment present special challenges for data protection and access control. Attribute-based encryption(ABE) not only protects data secrecy, but also has ciphertexts or decryption keys associated with fine-grained access policies that are automatically enforced during the decryption process. This enforcement puts data access under control at each data item level. However, ABE schemes have practical limitations on dynamic user revocation. In this paper, we propose two generic user revocation systems for ABE with user privacy protection, user revocation via ciphertext re-encryption(UR-CRE) and user revocation via cloud storage providers(UR-CSP), which work with any type of ABE scheme to dynamically revoke users.
The defense techniques for machine learning are critical yet challenging due tothe number and type of attacks for widely applied machine learning algorithms aresignificantly increasing. Among these attacks, the poison...
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The defense techniques for machine learning are critical yet challenging due tothe number and type of attacks for widely applied machine learning algorithms aresignificantly increasing. Among these attacks, the poisoning attack, which disturbsmachine learning algorithms by injecting poisoning samples, is an attack with the greatestthreat. In this paper, we focus on analyzing the characteristics of positioning samples andpropose a novel sample evaluation method to defend against the poisoning attack cateringfor the characteristics of poisoning samples. To capture the intrinsic data characteristicsfrom heterogeneous aspects, we first evaluate training data by multiple criteria, each ofwhich is reformulated from a spectral clustering. Then, we integrate the multipleevaluation scores generated by the multiple criteria through the proposed multiplespectral clustering aggregation (MSCA) method. Finally, we use the unified score as theindicator of poisoning attack samples. Experimental results on intrusion detection datasets show that MSCA significantly outperforms the K-means outlier detection in terms ofdata legality evaluation and poisoning attack detection.
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