41889531
Research on Pairwise Key Establishment Model and Algorithm for Sensor Networks
Security schemes of pairwise key establishment, which enable sensors to communicate with each other securely, play a fundamental role in research on security issue in wireless sensor networks. A new kind of pairwise key pre-distribution model for sensor networks is proposed. And in addition, based on which, an efficient dynamic key path establishment algorithm is designed. Theoretic analysis and experimental figures show that the new algorithm has better performance than those previous related works.
16518214
Key Distribution for Group-based Sensor Deployment Using a Novel Interconnection Graph
In this paper, we propose a pairwise key distribution scheme based on a novel interconnection graph termed Hierarchical Hypercube. Motivated by the fact that sensor nodes are often deployed in groups (for example, dropped from an airplane at different locations) and hence the whole network is composed of multiple such groups, we design Hierarchical Hypercube as a two layer topology, where each group is modeled by an inner hypercube and connections between the groups are modeled using an outer hypercube. we propose a topology termed Hierarchy Hypercube. While retaining the desirable properties already shown by existing pairwise schemes, by using Hierarchical Hypercube, direct communication from a node to any other nodes is not required, either within a group or among groups, and hence this topology can effectively and realistically reflect the connectivity of the physical sensor network deployed in groups. Furthermore, we propose key pre-distribution scheme based on this novel topology and show that the new scheme possesses high probability of direct key establishment, low memory overhead, and resilience in the presence of broken communication links and compromised nodes, and thus still retain the desirable properties even when the ideal logical connection are distorted in the real deployment.
6464793
Ringed Petersen spheres connected hypercube interconnection networks
On the basis of the short diameter of Petersen graph, and high connectivity of hypercube, a novel interconnection networks, RPSCH(n) (ringed Petersen spheres connected hypercube) is proposed, and whose characteristics are studied simultaneously. It is proved that RPSCH(n) is not only of regularity and good extensibility, but also of shorter diameter and better connectivity than those previous well-known interconnection networks such as Q/sub n/, RP(n) and HP(n). In addition, the unicasting and broadcasting routing algorithms are designed for RPSCH(n), whose communication efficiency are proved to be n-2 and n-2 respectively.
11558298
Layer Key Management Scheme on Wireless Sensor Networks
Wireless sensor networks are open architectures, so any potential threat can easily intercept, wiretap and counterfeit the information. Therefore, the safety of WSN is very important. Since any single key system cannot guarantee the security of the wireless sensor network for communications, this paper introduces a hierarchical key management scheme based on the different abilities of different sensor nodes in the clustered wireless sensor network. In this scheme, the nodes are distributed into several clusters, and a cluster head must be elected for each cluster. Private communication between cluster heads is realized through the encryption system based on the identity of each head while private communication between cluster nodes in a same cluster head is achieved through the random key preliminary distribution system. For cluster head node plays a pivotal role in this scheme, a trust management system should be introduced into the election of the cluster head which will exclude the malicious node from outside the cluster, thus improve the whole network security.
45075264
Multi-path GEM for Routing in Wireless Sensor Networks
Wireless sensor networks are expected to be used in many different applications such as disaster relief and environmental control. Efficient routing protocols need to be thoroughly studied before wireless sensor networks are widely deployed. GEM is an ingenious routing algorithm that is based on the idea of graph embedding. Using a well designed virtual coordinate system, GEM provides a remarkably simple route selection mechanism. However, GEM does not survive edge failures well. In this paper, we propose R-GEM and S-GEM that use the idea of GEM and improve its reliability performance significantly. Both of them outperform GEM in all of the experimental scenarios. Specifically, in the case that 2% of all edges in the network fail to transfer packets, when only disjoint pairs are taken into consideration, GEM leads to a path error rate of 10% while R-GEM and S-GEM only result in a path error rate of 2%.
2723209
An energy-efficient skew compensation technique for high-speed skew-sensitive signaling
Presented in this paper is an energy-efficient skew compensation technique referred to as the resistance-based distributed clock deskew (RDSK) technique. In comparison with the existing clock deskew techniques, the proposed RDSK technique achieves significant power reduction while maintaining effective in skew management. Furthermore, the proposed RDSK provides a better scalability for future technology scaling. Simulation results of an on-chip interconnect communication system with distributed receivers designed in a 0.10 /spl mu/m CMOS process demonstrate 20%-51% power reduction over the existing techniques. The proposed RDSK is an essential element of our adaptive timing framework where circuit performance can be adjusted adaptively in response to time-varying disturbances and uncertainties.
10512188
An immune neural network used for classification
Based on an analysis of immune phenomena in nature and utilizing performances of ANN, a novel network mode, i.e., an immune neural network (INN), is proposed which integrates the immune mechanism and the function of neural information processing. The learning algorithm of an INN selects an excitation function and adaptive algorithm of the network. This model makes it easy for a user to utilize directly the characteristic information of a problem and to simplify the original structure by adjusting the excitation function with prior knowledge, improving the working efficiency and searching accuracy. A theoretical analysis and a simulation test for the twin-spiral problem show that, compared with an artificial neural network, INN is not only effective but also feasible. INN can simplify the structure of the existent model and show good working performance.
5763461
AdaBoost with SVM-based component classifiers
The use of SVM (Support Vector Machine) as component classifier in AdaBoost may seem like going against the grain of the Boosting principle since SVM is not an easy classifier to train. Moreover, Wickramaratna et al. [2001. Performance degradation in boosting. In: Proceedings of the Second International Workshop on Multiple Classifier Systems, pp. 11-21] show that AdaBoost with strong component classifiers is not viable. In this paper, we shall show that AdaBoost incorporating properly designed RBFSVM (SVM with the RBF kernel) component classifiers, which we call AdaBoostSVM, can perform as well as SVM. Furthermore, the proposed AdaBoostSVM demonstrates better generalization performance than SVM on imbalanced classification problems. The key idea of AdaBoostSVM is that for the sequence of trained RBFSVM component classifiers, starting with large @s values (implying weak learning), the @s values are reduced progressively as the Boosting iteration proceeds. This effectively produces a set of RBFSVM component classifiers whose model parameters are adaptively different manifesting in better generalization as compared to AdaBoost approach with SVM component classifiers using a fixed (optimal) @s value. From benchmark data sets, we show that our AdaBoostSVM approach outperforms other AdaBoost approaches using component classifiers such as Decision Trees and Neural Networks. AdaBoostSVM can be seen as a proof of concept of the idea proposed in Valentini and Dietterich [2004. Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods. Journal of Machine Learning Research 5, 725-775] that Adaboost with heterogeneous SVMs could work well. Moreover, we extend AdaBoostSVM to the Diverse AdaBoostSVM to address the reported accuracy/diversity dilemma of the original Adaboost. By designing parameter adjusting strategies, the distributions of accuracy and diversity over RBFSVM component classifiers are tuned to maintain a good balance between them and promising results have been obtained on benchmark data sets.